Generally, we use the hyperparameters provided by the authors of the respective papers. We train all models on a horizon of 12 timesteps, where the training loss is the mean absolute error of all 12 time steps. For all datasets except SWaT and WADI, we ignore targets with value zero (as in [1]}, [2]}), as these correspond to missing values. We train models for a maximum of 200 epochs and use early stopping with patience of 20 epochs. We use the validation MAE for early stopping. For SWaT and WADI as well as DAG and Diffusion, we fix the window length to 20. For METR-LA and PEMS-BAY, we set the window length to 12, as proposed by [2]}, [1]}. For Electricity, Solar Energy, Traffic, and Exchange Rate, window size is 168 as in [2]}.
